vaeac_train_model_continue: Continue to Train the vaeac Model

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

Continue to Train the vaeac Model

Description

Function that loads a previously trained vaeac model and continue the training, either on new data or on the same dataset as it was trained on before. If we are given a new dataset, then we assume that new dataset has the same distribution and one_hot_max_sizes as the original dataset.

Usage

vaeac_train_model_continue(
  explanation,
  epochs_new,
  lr_new = NULL,
  x_train = NULL,
  save_data = FALSE,
  verbose = NULL,
  seed = 1
)

Arguments

explanation	A explain() object and vaeac must be the used approach.
epochs_new	Positive integer. The number of extra epochs to conduct.
lr_new	Positive numeric. If we are to overwrite the old learning rate in the adam optimizer.
x_train	A data.table containing the training data. Categorical data must have class names 1,2,\dots,K.
save_data	Logical (default is FALSE). If TRUE, then the data is stored together with the model. Useful if one are to continue to train the model later using vaeac_train_model_continue().
verbose	String vector or NULL. Controls verbosity (printout detail level) via one or more of "basic", "progress", "convergence", "shapley" and "vS_details". "basic" (default) displays basic information about the computation and messages about parameters/checks. "progress" displays where in the calculation process the function currently is. "convergence" displays how close the Shapley value estimates are to convergence (only when iterative = TRUE). "shapley" displays intermediate Shapley value estimates and standard deviations (only when iterative = TRUE), and the final estimates. "vS_details" displays information about the v(S) estimates, most relevant for approach %in% c("regression_separate", "regression_surrogate", "vaeac"). NULL means no printout. Any combination can be used, e.g., verbose = c("basic", "vS_details").
seed	Positive integer (default is 1). Seed for reproducibility. Specifies the seed before any randomness based code is being run.

Value

A list containing the training/validation errors and paths to where the vaeac models are saved on the disk.

Author(s)

Lars Henry Berge Olsen

References

• Olsen, L. H., Glad, I. K., Jullum, M., & Aas, K. (2022). Using Shapley values and variational autoencoders to explain predictive models with dependent mixed features. Journal of machine learning research, 23(213), 1-51

shapr documentation built on Aug. 25, 2025, 5:11 p.m.